Impact of Covid-19 on households in the Northern States of Malaysia and the potential policy recommendations
Notice bibliographique
Résumé
The purpose of this study is the determine the impact of COVID-19 on households in the Northern States of Malaysia namely Perlis, Kedah, Pulau Pinang and Perak. The first main objective of this study was to analyse the impact from the perspective of socioeconomy and sectoral performance as well as the impact on salaries and wages based on available secondary data in 2020 and up to Quarter 1, 2021. From the analysis, the impact of COVID-19 was significant as Gross Domestic Product (GDP) for Malaysia and all the four States contracted in 2020 albeit all the northern States with exception of Perlis fared much better than the national average. The second main objective of the study was to determine the current impact of COVID-19, the author conducted an online survey for a period between week 2 and week 3 of July 2021, at a time where Malaysia was in the mode of full lockdown and no inter-district and interstate travels were allowed. For the online survey, 19% of respondents reported their incomes decreased during the time of pandemic in comparison to before the pandemic, 73.2% of respondents reported no change in their incomes and 2.4% reported an increase in their incomes. Unfortunately, 3.4% of respondents reported that they have no income during the current pandemic. Based on the data analysis from the primary data collection, gender was a significant predictor in the model used by the author and while educational attainment, income before the pandemic and marital status were other significant predictors. This showed that female was less likely to suffer a decrease in income compared to male during the pandemic. While the higher education attainment group was less likely to suffer a decrease in income during the pandemic. The same probability was for the group with higher income before the pandemic and the group which was married compared to the single group. Some of these factors – long-standing status markers, which have been exacerbated by COVID-19, may lead towards increased inequalities within the society and create further disparities between income groups. The third objective of this study is to recommend additional policies that can be implemented to alleviate the impact of COVID-19 including by helping the vulnerable groups, realising the approved investment, dealing with the impact on tax incentives caused by Pillar 2 solutions proposed by OECD, and expediting the recovery of the tourism sector.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».